DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward
DARLR optimizes recommender systems with dynamic rewards, showing a 16% improvement on KuaiRand.
Key Findings
Methodology
DARLR employs a dual-agent framework with a selector and a recommender. The selector identifies reference users by balancing similarity and diversity, while the recommender aggregates information to dynamically refine reward estimations. Statistical features guide dynamic uncertainty penalty adaptation.
Key Results
- On the KuaiRand dataset, DARLR's dynamic reward mechanism improved cumulative rewards by 16%, converging faster than static rewards.
- On the KuaiRec dataset, dynamic rewards significantly outperformed static rewards.
- Ablation studies confirmed the critical role of the selector and recommender's synergy in performance enhancement.
Significance
DARLR addresses inaccuracies in reward functions, enhancing long-term user satisfaction in recommender systems, with broad application potential.
Technical Contribution
Introduced a dual-agent framework that dynamically updates world models, significantly improving recommendation policy effectiveness. Offers new theoretical guarantees and engineering possibilities compared to existing methods.
Novelty
DARLR is the first to introduce dynamic reward shaping in recommender systems, overcoming limitations of static reward functions with a dual-agent framework.
Limitations
- The selector's reference user selection may be affected by dataset sparsity, impacting recommendation quality.
- Dynamic reward mechanisms require additional computational resources, increasing system complexity.
Future Work
Future research could explore optimizing selector performance on larger datasets and reducing computational costs of dynamic reward mechanisms.
AI Executive Summary
Recommender systems have achieved remarkable success in fields like e-commerce and social media, yet existing methods struggle to capture users' long-term dynamic interests. DARLR introduces a dual-agent offline reinforcement learning framework that dynamically updates world models to optimize recommendation policies. The selector identifies reference users, while the recommender aggregates their information to dynamically adjust reward estimations. Experimental results demonstrate DARLR's superior performance across multiple benchmark datasets, particularly in dynamic reward shaping. However, the selector's performance may be influenced by data sparsity, and future research could further optimize its efficiency.
Deep Analysis
Background
Recommender systems are widely used in industrial applications, typically employing supervised learning and reinforcement learning methods. Supervised learning struggles to capture users' long-term dynamic interests, while reinforcement learning optimizes user satisfaction through interactive training.
Core Problem
Existing offline reinforcement learning methods use static reward functions, leading to inaccurate reward estimations that affect recommendation policy quality.
Innovation
DARLR dynamically updates world models with a dual-agent framework, where the selector identifies reference users and the recommender aggregates information to dynamically refine reward estimations.
Methodology
- �� World model trained via supervised learning, using DeepFM for reward prediction
- �� Selector identifies reference users, balancing similarity and diversity
- �� Recommender aggregates information, dynamically adjusts reward estimations and uncertainty penalties
Experiments
Experiments conducted on KuaiRand, KuaiRec, and two other benchmark datasets, comparing dynamic and static rewards, with ablation studies verifying the synergy of the selector and recommender.
Results
DARLR improved cumulative rewards by 16% on KuaiRand, with dynamic rewards converging faster and performing better than static rewards.
Applications
DARLR can be applied to e-commerce and social media recommender systems, enhancing user satisfaction and long-term engagement.
Limitations & Outlook
The selector's performance may be affected by data sparsity, and dynamic reward mechanisms increase computational complexity.
Plain Language Accessible to non-experts
Imagine a chef in a kitchen preparing dinner. The chef has a recipe but lacks detailed instructions for each step. To ensure each dish is perfect, the chef observes other chefs' methods and adjusts their own steps based on feedback. This is like DARLR's selector and recommender, where the selector chooses reference users and the recommender dynamically adjusts recommendation strategies based on their information.
ELI14 Explained like you're 14
Imagine playing a game with many levels, each with different challenges. To win, you need to watch other players' strategies and adjust your own based on their feedback. This is like DARLR's selector and recommender, where the selector chooses reference users and the recommender dynamically adjusts strategies based on their information.
Glossary
Reinforcement Learning
A machine learning method that optimizes strategies through interaction with the environment.
Used in recommender systems to optimize long-term user satisfaction.
Recommender System
An information filtering system that recommends content by analyzing user data.
DARLR is used to optimize recommendation strategies.
Dynamic Reward
A reward mechanism that continuously updates reward estimations to optimize strategies.
DARLR uses dynamic reward shaping to improve recommendation quality.
Selector
An agent in DARLR responsible for selecting reference users.
The selector balances similarity and diversity in user selection.
Recommender
An agent in DARLR responsible for aggregating information and adjusting reward estimations.
The recommender dynamically adjusts strategies based on the selector's information.
Open Questions Unanswered questions from this research
- 1 Optimizing selector performance on large datasets remains unexplored.
- 2 Reducing computational costs of dynamic reward mechanisms is an open question.
Applications
Immediate Applications
E-commerce Recommendation
DARLR can be used on e-commerce platforms to enhance user satisfaction and sales.
Long-term Vision
Social Media Recommendation
DARLR can be applied to social media platforms to increase user interaction and engagement.
Abstract
Model-based offline reinforcement learning (RL) has emerged as a promising approach for recommender systems, enabling effective policy learning by interacting with frozen world models. However, the reward functions in these world models, trained on sparse offline logs, often suffer from inaccuracies. Specifically, existing methods face two major limitations in addressing this challenge: (1) deterministic use of reward functions as static look-up tables, which propagates inaccuracies during policy learning, and (2) static uncertainty designs that fail to effectively capture decision risks and mitigate the impact of these inaccuracies. In this work, a dual-agent framework, DARLR, is proposed to dynamically update world models to enhance recommendation policies. To achieve this, a \textbf{\textit{selector}} is introduced to identify reference users by balancing similarity and diversity so that the \textbf{\textit{recommender}} can aggregate information from these users and iteratively refine reward estimations for dynamic reward shaping. Further, the statistical features of the selected users guide the dynamic adaptation of an uncertainty penalty to better align with evolving recommendation requirements. Extensive experiments on four benchmark datasets demonstrate the superior performance of DARLR, validating its effectiveness. The code is available at https://github.com/ArronDZhang/DARLR.